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Application of Deep Learning in Image Recognition of Citrus Pests

Author

Listed:
  • Xinyu Jia

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Xueqin Jiang

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Zhiyong Li

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China)

  • Jiong Mu

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China)

  • Yuchao Wang

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Yupeng Niu

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    Ya’an Digital Agricultural Engineering Technology Research Center, Ya’an 625000, China)

Abstract

The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Generative Adversarial Network (AR-GAN) data augmentation technique, then we built Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and MobileNet citrus pest recognition models by using transfer learning, and finally, we introduced an appropriate attention mechanism according to the model characteristics to enhance the ability of the three models to operate effectively in complex, real environments with greater emphasis placed on incorporating the deep features of the pests themselves. The results showed that the average recognition accuracy of the three models reached 93.65%, the average precision reached 93.82%, the average recall reached 93.65%, and the average F1-score reached 93.62%. The integrated application of data augmentation, transfer learning and attention mechanisms in the research can significantly enhance the model’s ability to classify citrus pests while saving training cost and time, which can be a reference for researchers in the industry or other fields.

Suggested Citation

  • Xinyu Jia & Xueqin Jiang & Zhiyong Li & Jiong Mu & Yuchao Wang & Yupeng Niu, 2023. "Application of Deep Learning in Image Recognition of Citrus Pests," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1023-:d:1141266
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    References listed on IDEAS

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    1. Makorere, Robert, 2014. "An Exploration Of Factors Affecting Development Of Citrus Industry In Tanzania: Empirical Evidence From Muheza District, Tanga Region," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 2(2), pages 1-20, April.
    2. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    3. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
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    Cited by:

    1. Yuzhe Bai & Fengjun Hou & Xinyuan Fan & Weifan Lin & Jinghan Lu & Junyu Zhou & Dongchen Fan & Lin Li, 2023. "A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques," Agriculture, MDPI, vol. 13(9), pages 1-23, September.

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